def test_max_against_brute_force(self):
maxes = [max_brute_force(self._weights[i])
for i in range(self._weights.shape[0])]
maxes2 = npy_ops.soft_sw_affine(self._toy_sub.numpy(),
self._toy_gap_open.numpy(),
self._toy_gap_extend.numpy(),
temperature=0)
self.assertAllClose(maxes, maxes2)
def test_lse_against_brute_force(self):
for temp in (1e-3, 1.0, 1e3):
softmaxes = np.array([lse_brute_force(self._weights[i],
temperature=temp)
for i in range(self._weights.shape[0])])
softmaxes2 = npy_ops.soft_sw_affine(self._toy_sub.numpy(),
self._toy_gap_open.numpy(),
self._toy_gap_extend.numpy(),
temperature=temp)
self.assertAllClose(softmaxes, softmaxes2)
def test_perturbation_friendly_version_against_numpy_version(self, decorator):
smith_waterman_fn = tf_ops.hard_sw_affine
if decorator is not None:
smith_waterman_fn = decorator(smith_waterman_fn)
maxes, _ = smith_waterman_fn(self._w)
maxes2 = npy_ops.soft_sw_affine(self._toy_sub.numpy(),
self._toy_gap_open.numpy(),
self._toy_gap_extend.numpy(),
temperature=0)
self.assertAllClose(maxes, maxes2)
def test_gradient_shape(self):
toy_sub = self._toy_sub.numpy()
toy_gap_open = self._toy_gap_open.numpy()
toy_gap_extend = self._toy_gap_extend.numpy()
values, g_sim_mat, g_gap_open, g_gap_extend = npy_ops.soft_sw_affine(
sim_mat=toy_sub,
gap_open=toy_gap_open,
gap_extend=toy_gap_extend,
ret_grads=True)
self.assertEqual(values.shape[0], toy_sub.shape[0])
self.assertAllEqual(g_sim_mat.shape, toy_sub.shape)
self.assertAllEqual(g_gap_open.shape, toy_gap_open.shape)
self.assertAllEqual(g_gap_extend.shape, toy_gap_extend.shape)
def test_soft_version_against_numpy_version(self, decorator, temp):
soft_version_fn = tf_ops.soft_sw_affine_fwd
if decorator is not None:
soft_version_fn = decorator(soft_version_fn)
maxes = npy_ops.soft_sw_affine(self._toy_sub.numpy(),
self._toy_gap_open.numpy(),
self._toy_gap_extend.numpy(),
temperature=temp)
maxes2 = soft_version_fn(self._toy_sub,
self._toy_gap_open,
self._toy_gap_extend,
temp=temp)
self.assertAllClose(maxes, maxes2)
def test_max_against_sw_general(self):
def make_gap_func(gap_open, gap_extend):
def gap_func(k):
return gap_open + gap_extend * (k-1)
return gap_func
toy_sub = self._toy_sub.numpy()
toy_gap_open = self._toy_gap_open.numpy()
toy_gap_extend = self._toy_gap_extend.numpy()
maxes = [_sw_general(toy_sub[i],
make_gap_func(toy_gap_open[i], toy_gap_extend[i]))
for i in range(self._weights.shape[0])]
maxes2 = npy_ops.soft_sw_affine(toy_sub, toy_gap_open, toy_gap_extend,
temperature=0)
self.assertAllClose(maxes, maxes2)